Contact centers, such as Automatic Call Distribution or ACD systems, are employed by many enterprises to service customer contacts. A typical contact center includes a switch and/or server to receive and route incoming packet-switched and/or circuit-switched contacts and one or more resources, such as human agents and automated resources (e.g., Interactive Voice Response (IVR) units), to service the incoming contacts. Contact centers distribute contacts, whether inbound or outbound, for servicing to any suitable resource according to predefined criteria. In many existing systems, the criteria for servicing the contact from the moment that the contact center becomes aware of the contact until the contact is connected to an agent are customer-specifiable (i.e., programmable by the operator of the contact center), via a capability called vectoring. Normally in present-day ACDs when the ACD system's controller detects that an agent has become available to handle a contact, the controller identifies all predefined contact-handling skills of the agent (usually in some order of priority) and delivers to the agent the highest-priority oldest contact that matches the agent's highest-priority skill. Generally, the only condition that results in a contact not being delivered to an available agent is that there are no contacts waiting to be handled.
The primary objective of contact center management, including call-distribution algorithms, is to ultimately maximize contact center performance and profitability. An ongoing challenge in contact center administration is monitoring of agent behaviors to optimize the use of contact center resources and maximize agent performance and profitably. Agents can take many actions to “beat the system” or skew contact center statistics to “hide” deviant agent performance. Such problem agents are distinguished from poorly performing agents because they try to take advantage of the system to benefit themselves. Problem agents may behave in such a way that their metrics indicate that they are high performing agents when in fact they are not. For instance, an agent can have a long contact during which he or she is chit-chatting and follow that contact with a short contact during which the agent fails properly to service the customer so that the average duration of the two contacts falls within contact center targets. Because deviant behaviors are often varied and infrequent, they are hard to detect and correct. It is even more difficult to understand the reason for the behavior. For example, is the agent dumping a call so he or she can go to break on time?
Current products for monitoring and reporting on contact center performance are generally unable to isolate problem behaviors and identify contextual patterns. Current contact center reporting products monitor and report on the performance of agents. Generally, these are summary statistics, such as how many contacts were handled, average handle time, and result codes. Call Management System or CMS™ by Avaya, Inc., reports on a few problem behaviors in isolation. These behaviors include transfers, redirects from time out, disconnects from hold, long hold, aux time, direct time, conferences, contacts of less than a determined time or “short contacts”, contacts of greater than a determined time or “long contacts”, long alerts, and long wrap-ups. Nice Analyzer™ allows one to do ad-hoc queries to search for events, such as abandons from hold. The products fail to provide a systematic method for discovering beneficial or problem behaviors, reporting on combinations of problems, facilitating an understanding of what might be happening around the behavior that is contributing to it, and allowing the administrator to identify the behavior by type of contact being handled, e.g., inbound vs. outbound, direct vs. ACD delivered, and internal vs. external.